1. Field
The embodiments described below relate generally to systems for storing and providing historical operations data.
2. Discussion
Conventional industrial systems often rely to some extent on computer-based automation and monitoring. In some examples of automation and monitoring, data arising from the operation of a manufacturing plant is acquired, analyzed and responded to if necessary. The data may arise from independent sources, with each source configured to provide substantially raw or native “point” data at pre-defined intervals in real or near real-time. The point data may be presented to an operator in real or near-real time, and may include such as numerical values produced by gauges and/or monitors (e.g., speed, temperature, or pressure).
Examples of systems that may acquire, analyze, and act on point data include industrial automation systems, supervisory control and data acquisition (SCADA) systems, and general data acquisition systems. In such systems, point data may be associated with a “tag” to create a structural data element that is made accessible to other components, systems, applications and/or users. In general, point data obtained from selected sources is subject to dynamic change and is monitored and reported through various operations and functions associated with processing the point data. In industrial automation and control systems, decision support and reporting capabilities may be provided based on tag-associated point data that is monitored over very short timeframes ranging in the sub-second to sub-minute range.
Many conventional systems provide only limited capabilities to access, interpret, and/or manipulate tag-based point data collectively or in connection with “non-point” data. Non-point data relates to a broad category of context-providing information that is associated with point data and may extend the functionality and meaning of the point data. Non-point data may include descriptive and/or attribute information characterizing the point data, as well as, other information such as limits, ranges, etc. In conventional systems, integral and flexible manipulation of tag-based point data and non-point data is restricted due to the inherent differences between and properties of the two types of data.
Conventional systems also possess a limited ability to integrate and relate tag-based point data and non-tag-based data. Non-tag-based data may originate from numerous sources and relate to disparate aspects of an enterprise environment. For example, non-tag-based data may comprise data associated with conventional database applications/environments and include transactional information, production data, business data, etc. Conventionally, attempts to integrate non-tag-based data with tag-based point data may be hindered or prevented completely as a consequence of underlying differences in structure and content between these data types. As a result, generating and implementing logical constructions or schema in which both tag-based data and non-tag-based data are integrally used is problematic in conventional systems. Such limitations limit overall flexibility and increase the difficulty of scaling to complex, enterprise-level environments.
Another important consideration to the integral management of point data and non-point data relates to the recognition of differences in desirable update or acquisition frequencies. The dynamic properties of point data give rise to time critical retrieval restrictions on systems designed to acquire and evaluate point data. Rapidly-changing point data is generally acquired or refreshed at a high frequency to insure that the information is up-to-date. Other point data and non-point data may be more static in nature and may not require high-frequency acquisition.
Conventional systems are not well suited to provide integration of customizable data-dependent acquisition strategies or associated acquisition rates. As a result, these systems experience reduced performance, especially in complex environments where data or values to be retrieved possess different optimal or desired refresh rates. Furthermore, these conventional systems fail to provide the ability to efficiently customize or configure differential acquisition strategies for point data and non-point data so as to improve overall system performance.
The foregoing difficulties in managing tag-based point data, non-point data, and non-tag-based data also hinder efficient analysis and reporting of such data. Conventional systems such as those described above may therefore not be suitable for historical analysis and evaluation of acquired data. Accordingly, improved systems for analysis, manipulation and/or reporting of operations data are desired.